4 Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput, 2003, 7: 76-80
[4]
5 Fang L, Kim H, LeFevre K, et al. A privacy recommendation wizard for users of social networking sites. In: Chen Y, Danezis G, Shmatikov V, eds. Proceedings of the 17th International Conference on Computer and Communications Security. New York: ACM, 2010. 630-632
[5]
10 Cacheda F, Carneiro V, Fernández D, et al. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans Web, 2011, 5: 2
[6]
11 Gunes I, Kaleli C, Bilge A, et al. Shilling attacks against recommender systems: A comprehensive survey. Artif Intell Rev, 2012, doi: 10.1007/s10462-012-9364-9
[7]
12 Williams C, Mobasher B. Profile injection attack detection for securing collaborative recommender systems. Technical Report, Computer Science, DePaul University. 2006
[8]
16 Mehta B, Nejdl W. Unsupervised strategies for shilling detection and robust collaborative filtering. User Model User-Adap, 2009, 19: 65-97
[9]
17 Chirita P, Nejdl W, Zamfir C. Preventing shilling attacks in online recommender systems. In: Bonifati A, Lee D, eds. Proceedings of the 7th International Workshop on Web Information and Data Management. New York: ACM, 2005. 67-74
[10]
18 Burke R, Mobasher B, Williams C, et al. Classification features for attack detection in collaborative recommendation systems. In: Bonifati A, Fundulaki I, eds. Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2006. 542-547
[11]
21 Mehta B, Hofmann T, Fankhauser P. Lies and propaganda: detecting spam users in collaborative filtering. In: Chin D N, Zhou M X, Lau T A, et al, eds. Proceedings of the 12th International Conference on Intelligent User Interfaces. New York: ACM, 2007. 14-21
[12]
22 Bryan K, O'Mahony M P, Cunningham P. Unsupervised retrieval of attack profiles in collaborative recommender systems. Technical Report, University College Dublin, 2008
[13]
23 Lee J, Zhu D. Shilling attack detection—A new approach for a trustworthy recommender system. Informs J Comput, 2012, 24: 117-131
[14]
30 O'Mahony M P, Hurley N J, Silvestre G C M. Recommender systems: Attack types and strategies. In: Anderson M, Oates T, eds. Proceedings of the 20th National Conference on Artificial Intelligence. USA: MIT Press, 2005. 334-339
[15]
31 Zheng S, Tao J, Baras J S. A robust collaborative filtering algorithm using ordered logistic regression. In: Hagimoto K, Ueda H, Jamallipour A, eds. Proceedings of the 17th International Conference on Communications. New York: IEEE, 2011. 1-6
[16]
32 Su X F, Zeng H J, Chen Z. Finding group shilling in recommendation system. In: Ellis A, Hagino T, eds. Proceedings of the 14th International Conference on World Wide Web. New York: ACM, 2005. 960-961
[17]
35 Lim E, Nguyen V, Jindal N, et al. Detecting product review spammers using rating behaviors. In: Huang J, Koudas N, Jones G J F, et al, eds. Proceedings of the 19th International Conference on Information and Knowledge Management. New York: ACM, 2010. 939-948
[18]
36 Wang G, Xie S H, Liu B, et al. Review graph based online store review spammer detection. In: Cook D J, Pei J, Wang W, et al, eds. Proceedings of the 11th International Conference on Data Mining. New York: IEEE, 2011. 1242-1247
[19]
39 Cheng Z P, Hurley N. Effective diverse and obfuscated attacks on model-based recommender systems. In: Bergman L D, Tuzhilin A, Burke R, et al, eds. Proceedings of the 3rd International Conference on Recommender Systems. New York: ACM, 2009. 141-148
[20]
3 Bell R M, Koren Y. Improved neighborhood-based collaborative filtering. In: Berkhin P, Caruana R, Wu X D, eds. Proceedings of the 13th International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2007. 7-14
[21]
6 Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating collaborative filtering recommender systems. ACM Trans Inform Syst, 2004, 22: 5-53
[22]
7 Lam S K, Riedl J. Shilling recommender systems for fun and profit. In: Feldman S I, Uretsky M, Najork M, et al, eds. Proceedings of the 13th International Conference on World Wide Web. New York: ACM, 2004. 393-402
13 Zhang S, Chakrabarti A, Ford J, et al. Attack detection in time series for recommender systems. In: Eliassi-Rad T H, Ungar L, Craven M, et al, eds. Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2006. 809-814
[26]
14 Hurley N J, Cheng Z P, Zhang M. Statistical attack detection. In: Bergman L D, Tuzhilin A, Burke R, et al, eds. Proceedings of the 3rd International Conference on Recommender Systems. New York: ACM, 2009. 149-156
[27]
15 Liu Q, Chen E H, Xiong H, et al. Enhancing collaborative filtering by user interest expansion via personalized ranking systems. IEEE Trans Syst Man Cy B, 2012, 42: 218-233
[28]
19 Burke R, Williams C, Bhaumik R. Segment-based injection attacks against collaborative filtering recommender systems. In: Han J W, Wah B, eds. Proceedings of the 5th International Conference on Data Mining. New York: IEEE, 2005. 577-580
24 Zhang S, Ouyang Y, Ford J, et al. Analysis of a low-dimensional linear model under recommendation attacks. In: Efthimiadis E N, Dumais S T, Hawking D, et al, eds. Proceedings of the 29th International Conference on Research and Development in Information Retrieval. New York: ACM, 2006. 517-524
26 Zhou Z H, Li M. Tri-Training: Exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data En, 2005, 17: 1529-1541
[33]
27 Wu Z A, Cao J, Mao B, et al. Semi-SAD: Applying semi-supervised learning to shilling attack detection. In: Mobasher B, Burke R, Jannach D, et al, eds. Proceedings of the 5th International Conference on Recommender Systems. New York: ACM, 2011. 289-292
[34]
28 Cao J, Wu Z A, Mao B, et al. Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web, 2012, doi: 10.1007/s11280-012-0164-6
[35]
29 Wu Z A, Wu J J, Cao J, et al. HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation. In: Yang Q, Agarwal D, Pei J, et al, eds. Proceedings of the 18th International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2012. 985-993
[36]
33 Wang Y Q, Wu Z A, Cao J, et al. Towards a tricky group shilling attack model against recommender systems. In: Zhou S G, Karypis G, Zhang S M, eds. Proceedings of the 8th International Conference on Advanced Data Mining and Applications. Berlin: Springer, 2012. 675-688
[37]
34 Mukherjee A, Liu B, Glance N S. Spotting fake reviewer groups in consumer reviews. In: Mille A, Gandon F L, Misselis J, et al, eds. Proceedings of the 21st International Conference on World Wide Web. New York: ACM, 2012. 191-200
[38]
37 Sandvig J J, Mobasher B, Burke R. A survey of collaborative recommendation and the robustness of model-based algorithms. IEEE Data En Bull, 2008, 31: 3-13
[39]
38 Sandvig J J, Mobasher B, Burke R. Robustness of collaborative recommendation based on association rule mining. In: Konstan J A, Riedl J, Smyth B, eds. Proceedings of the 1st International Conference on Recommender Systems. New York: ACM, 2007. 105-112
[40]
40 Mehta B, Hofmann T, Nejdl W. Robust collaborative filtering. In: Konstan J A, Riedl J, Smyth B, eds. Proceedings of the 1st International Conference on Recommender Systems. New York: ACM, 2007, 49-56
[41]
41 Mehta B, Nejdl W. Attack resistant collaborative filtering. In: Myaeng S, Oard W D, Sebastiani F, et al, eds. Proceedings of the 31st International Conference on Research and Development in Information Retrieval. New York: ACM, 2008. 75-82